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Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and associated tools
- The AI development process: from training to deployment
Gaining Insight into the CANN Toolkit
- Defining CANN and explaining its significance
- An overview of key components (ATC, AscendCL, operator libraries)
- The role of CANN within AI inference pipelines
Initial Steps with MindSpore and CANN
- Configuring the environment (MindSpore + CANN + Python)
- Training a fundamental model using MindSpore
- Exporting and converting the model via ATC
Executing Inference on Ascend Devices
- Utilizing the OM model with AscendCL or Python APIs
- Basic input/output preprocessing techniques
- Verifying model outputs
Integration with Other Frameworks
- Overview of TensorFlow, PyTorch, and ONNX support
- Supported operators and known limitations
- Simple model conversion demonstration (e.g., from ONNX to OM)
Exploring the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, and sample code
- Overview of MindSpore Hub and the model zoo
- Community forums, events, and support channels
Recap and Future Directions
Requirements
- A fundamental grasp of machine learning and deep learning principles
- Basic proficiency in Python programming
- No previous experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning engineers exploring deployment strategies
- Academic students or researchers new to Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
7 Hours
